Using Machine learning to enable feedback controlled manufacture of self-assembled patterned materials

使用机器学习实现自组装图案材料的反馈控制制造

• 批准号: EP/T004533/1
• 负责人: Stephen Ebbens
• 金额: $ 32.23万
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FT004533%2F1
• 关键词: Using; Machine; learning; enable; feedback

Achieving control over materials' structure at small length scales is a critical requirement to realise new technologies with improved performance in areas of energy generation, energy storage and healthcare. To give some examples, solar cells, batteries and sensors for medical diagnostics can all benefit from nano and micron scale structuring. An attractive way to produce such structures is via self-assembly, where materials arrange themselves into well-defined regular patterns. One way to access this behaviour is by drying a suspension or solution containing the material of interest. For example, if a suspension of particles in a liquid is spread onto a solid support and left to dry, under some conditions very regular, repeating "crystalline" arrangements will result. These arrangements possess optical properties that can be used for sensors, and serve as templates to make efficient electrodes for batteries and solar cell materials. Other examples of useful structure formation processes in drying solutions include complex networks that form from multi-component mixtures, and regular crystalline structures, both of which can enhance the performance of solar cells if present at certain specific length scales. However, despite its simplicity and potential applications, at present self-assembly in drying solutions is mainly used as a research method to produce small quantities of material, and is not viewed as a routine manufacturing method. We believe that this is because self-assembly is highly sensitive to many parameters, which hampers reproducibility and requires time consuming optimisation to produce a given material, making it un-attractive for large scale manufacturing. Here, we plan to investigate if adding an automated control system to continuous self-assembly/solution structure based processes can overcome these obstacles. To implement the control system, we will make microscopic observations during self-assembly and use algorithms to adjust the manufacturing instruments parameters based on this feedback. This method will be used both to rapidly identify the parameters required to produce the ideal structure for a particular application, and also to maintain high quality uniform structure production during continuous manufacture of large amounts of material. Despite feedback being well established as an effective way to control manufacturing processes in other sectors, this method has not yet been deployed for self-assembly due to difficulties in observing the structure forming process, and the challenges of implementing the required algorithms to beneficially adjust parameters. Here we will use advances in real time monitoring of drying films, together with expertise in "machine learning" computer methods that are able to build models for complex behaviour, to overcome these challenges.

在小尺度上实现对材料结构的控制是实现新技术在能源生产、能源储存和医疗保健领域提高性能的关键要求。举例来说，用于医疗诊断的太阳能电池、电池和传感器都可以从纳米和微米尺度的结构中受益。一种有吸引力的生产这种结构的方法是通过自组装，材料将自己排列成明确的规则模式。获得这种行为的一种方法是干燥含有感兴趣物质的悬浮液或溶液。例如，如果液体中的悬浮粒子被扩散到固体载体上并晾干，在某些条件下，会产生非常规则的、重复的“晶体”排列。这些排列具有可用于传感器的光学特性，并可作为制造电池和太阳能电池材料的高效电极的模板。在干燥溶液中有用的结构形成过程的其他例子包括由多组分混合物形成的复杂网络，以及规则的晶体结构，如果存在于特定的长度尺度，这两种结构都可以提高太阳能电池的性能。然而，尽管其简单性和潜在的应用，目前在干燥溶液中的自组装主要是作为一种研究方法来生产小批量的材料，而不是被视为常规的制造方法。我们认为这是因为自组装对许多参数高度敏感，这阻碍了可重复性，并且需要耗时的优化来生产给定的材料，使其对大规模制造没有吸引力。在这里，我们计划研究在基于连续自组装/解决方案结构的过程中添加自动化控制系统是否可以克服这些障碍。为了实现控制系统，我们将在自组装过程中进行微观观察，并使用算法根据该反馈调整制造仪器参数。该方法既可用于快速识别为特定应用生产理想结构所需的参数，也可用于在大量材料的连续制造过程中保持高质量的均匀结构生产。尽管反馈作为控制其他领域制造过程的有效方法已经得到了很好的确立，但由于观察结构形成过程的困难，以及实施所需算法以有益地调整参数的挑战，这种方法尚未应用于自组装。在这里，我们将利用干燥薄膜实时监测方面的进步，以及能够为复杂行为建立模型的“机器学习”计算机方法的专业知识，来克服这些挑战。